I am running a Repeated Measures two-way ANCOVA. The model produces an Estimated Marginal Means table, but the values are exactly the same (to the hundredths decimal place) as the Means in the descriptive statistics, despite there being a note at the bottom of the EMM table indicating that "the covariates appearing in the model are evaluated at the following values:..."
Is this a bug, or could I be doing something wrong?
Update:
Responding to question below, I should note that I used the drop down menus to run the analysis; however, this is the code that is used when I 'paste' the code.
DATASET ACTIVATE DataSet1.
GLM FT10 FT11 FT12 FT13 FT14 FT15 FT16 FT17 FT18 FT19 FT110 FT111 FT20 FT21 FT22 FT23 FT24 FT25 FT26 FT27 FT28 FT29 FT210 FT211 WITH SpatialScore FPSRTScore LDMean VGTotal
/WSFACTOR=Matching 2 Polynomial Trial 12 Polynomial
/METHOD=SSTYPE(3)
/PLOT=PROFILE(Trial*Matching) TYPE=LINE ERRORBAR=CI MEANREFERENCE=NO AXIS=AUTO
/EMMEANS=TABLES(OVERALL) WITH(SpatialScore=MEAN FPSRTScore=MEAN LDMean=MEAN VGTotal=MEAN)
/EMMEANS=TABLES(Matching) WITH(SpatialScore=MEAN FPSRTScore=MEAN LDMean=MEAN VGTotal=MEAN)COMPARE ADJ(BONFERRONI)
/EMMEANS=TABLES(Trial) WITH(SpatialScore=MEAN FPSRTScore=MEAN LDMean=MEAN VGTotal=MEAN)COMPARE ADJ(BONFERRONI)
/EMMEANS=TABLES(Matching*Trial) WITH(SpatialScore=MEAN FPSRTScore=MEAN LDMean=MEAN VGTotal=MEAN)
/PRINT=DESCRIPTIVE ETASQ
/CRITERIA=ALPHA(.05)
/WSDESIGN=Matching Trial Matching*Trial
/DESIGN=SpatialScore FPSRTScore LDMean VGTotal.
This is expected behavior. The reason that the EMMEANS don't differ from the observed means is that the covariate adjustment is done at the cell level in terms of between-subjects effects, and you have only one cell because you don't have any between-subjects factors.
Related
I'm a stats newb and was told by my professor to run a MANOVA for something I was checking out. Basically, I wanted to see if there was an interaction between ethnicity and a certain quadrant grouping for a set of outcome variables that are subscales of an overall measure (ders_tot).
An ANCOVA (one DV) already found an interaction between ethnicity and the quadrant grouping for ders_tot.
My MANOVA output is showing me that with Pillai's/Wilks there is no significance (p = .098 for both), but in SPSS there is also a table of between-subjects effects automatically generated that indicates strong interaction significance for one particular outcome variable (p = .003). The other DVs are far from significance (some as high as p = .27 or p = .66).
Is my MANOVA significance (or lack thereof) being seriously skewed by the highly nonsignificant variables? Am I still "allowed" to run analysis on that one particular variable included in the MANOVA that suggests strong significance? I also have data viz/chart output that makes a strong case for analyzing that particular variable.
(EDIT: BELOW PROBLEM HAS BEEN FIXED)
[Also, I've noticed that one of my covariates is always being run in SPSS with 1 df when it should be 2. I've triple checked the variable type and added labels and all that, and can't get it to run appropriately. When I run the same analysis in R, df = 2. This isn't affecting my sig. findings by much, but it's driving me crazy!]
I am attempting to model an allocation problem via (quasi)convex optimization: given a matrix of unknowns X containing the amount of a certain product in a certain moment and its value C, i want to maximize the resulting income cp.sum(cp.multiply(X, C)).
Among close/equivalent solutions, i want to have the least amount of different products types in inventary, - cp.sum(cp.maximum(0, cp.sign(X)), which counts the number of non-null entries in X as a penalty.
According to cvxpy, both functions are quasiconcave (the first is affine, and the second quasilinear), but when i compose them linearly, cp.sum(cp.multiply(X, C)) - cp.sum(cp.maximum(0, cp.sign(X)), the resulting problem has an UNKNOWN curvature, and refuses to solve since not 'DQCP'.
The problem is simplified, as there are additional parameters and weights, and in the constrains X is bound to be non-negative and to satisfy space availability, but this simple version reproduces this unexpected behavior.
Is the bug in cvxpy or in my math?
The difference of two quasiconcave functions is not necessarily quasiconcave, so this operation is not permitted in CVXPY.
I have some features that are zero-centered values and supposed to represent change between a current value and previous value. Generally speaking i believe there should be some symmetry between these values. Ie. there should be roughly the same amount of positive values as negative values and roughly these values should operate on the same scale.
When i try to scale my samples using MaxAbsScaler, i notice that my negative values for this feature get almost completely drowned out by the positive values. And i don't really have any reason to believe my positive values should be that much larger than my negative values.
So what i've noticed is that fundamentally, the magnitude of percentage change values are not symmetrical in scale. For example if i have a value that goes from 50 to 200, that would result in a 300.0% change. If i have a value that goes from 200 to 50 that would result in a -75.0% change. I get there is a reason for this, but in terms of my feature, i don't see a reason why a change of 50 to 100 should be 3x+ more "important" than the same change in value but the opposite direction.
Given this information, i do not believe there would be any reason to want my model to treat a change of 200-50 as a "lesser" change than a change of 50-200. Since i am trying to represent the change of a value over time, i want to abstract this pattern so that my model can "visualize" the change of a value over time that same way a person would.
Right now i am solving this by using this formula
if curr > prev:
return curr / prev - 1
else:
return (prev / curr - 1) * -1
And this does seem to treat changes in value, similarly regardless of the direction. Ie from the example of above 50>200 = 300, 200>50 = -300. Is there a reason why i shouldn't be doing this? Does this accomplish my goal? Has anyone ran into similar dilemmas?
This is a discussion question and it's difficult to know the right answer to it without knowing the physical relevance of your feature. You are calculating a percentage change, and a percent change is dependent on the original value. I am not a big fan of a custom formula only to make percent change symmetric since it adds a layer of complexity when it is unnecessary in my opinion.
If you want change to be symmetric, you can try direct difference or factor change. There's nothing to suggest that difference or factor change are less correct than percent change. So, depending on the physical relevance of your feature, each of the following symmetric measures would be correct ways to measure change -
Difference change -> 50 to 200 yields 150, 200 to 50 yields -150
Factor change with logarithm -> 50 to 200 yields log(4), 200 to 50 yields log(1/4) = -log(4)
You're having trouble because you haven't brought the abstract questions into your paradigm.
"... my model can "visualize" ... same way a person would."
In this paradigm, you need a metric for "same way". There is no such empirical standard. You've dropped both of the simple standards -- relative error and absolute error -- and you posit some inherently "normal" standard that doesn't exist.
Yes, we run into these dilemmas: choosing a success metric. You've chosen a classic example from "How To Lie With Statistics"; depending on the choice of starting and finishing proportions and the error metric, you can "prove" all sorts of things.
This brings us to your central question:
Does this accomplish my goal?
We don't know. First of all, you haven't given us your actual goal. Rather, you've given us an indefinite description and a single example of two data points. Second, you're asking the wrong entity. Make your changes, run the model on your data set, and examine the properties of the resulting predictions. Do those properties satisfy your desired end result?
For instance, given your posted data points, (200, 50) and (50, 200), how would other examples fit in, such as (1, 4), (1000, 10), etc.? If you're simply training on the proportion of change over the full range of values involved in that transaction, your proposal is just what you need: use the higher value as the basis. Since you didn't post any representative data, we have no idea what sort of distribution you have.
I am given a financial time series that is characterized by a bunch of structural breaks, i.e. the series isn't moving (literally at all), but at some points in time the series jumps up or down. Then it stays at this level for a while until the series jumps again. So the time series basically looks like a step function.
My assumption is that these breaks come from some particular exogenous variables that are in the form of dummies. So if a particular exogenous variable takes on the value 1, (I assume) it is very likely that the series jumps.
My question is how I could model this particular time series (in a uni- or multivariate sense). I guess that standard AR(MA)-models are inappropriate. I was thinking about creating two binary variables that take on the value 1 if there's an upward (downward) break and 0 otherwise. Then I would run a dynamic probit model to test the probabilities that the exogenous variables trigger a break. What do you think about this idea? Or would you have other suggestions? Please note that I don't wanna test for structural breaks but rather formulate a time series model.
Did you try ARIMAX, TAR, or STAR models?
You said that you have time series data and you think this series is influanced by some exogeneous shocks. I think you need to include exogeneous variable in your time series analysis thats where ARIMAX comes. This modela allows you to include exogeneous variable in ARIMA model.
You also said that there are(is) structural breaks. Try Treshold AutoRegressive or Smoothed Treshold AutoRegressive. I hope this helps to find more materials about that models. Here is one click here
In mahout, I'm setting up a GenericUserBasedRecommender, pretty straight forward for now, typical settings.
In generating a "preference" value for an item, we have the following 5 data points:
Positive interest
User converted on item (highest possible sign of interest)
Normal like (user expressed interest, e.g. like buttons)
Indirect expression of interest (clicks, cursor movements, measuring "eyeballs")
Negative interest
Indifference (items the user ignored when active on other items, a vague expression of disinterest)
Active dislike (thumbs down, remove item from my view, etc)
Over what range I should express these different attributes, let's use a 1-100 scale for discussion?
Should I be keeping the 'Active dislike' and 'Indifference' clustered close together, for example, at 1 and 5 respectively, with all the likes clustered in the 90-100 range?
Should 'Indifference' and 'Indirect expressions of interest' by closer to the center? As in 'Indifference' in the 20-35 range and 'Indirect like' in the 60-70 range?
Should 'User conversion' blow the scale away and be heads and tails higher than the others? As in: 'User Conversion' # 100, 'Lesser likes' # ~65, 'Dislikes' clustered in the 1-10 range?
On the scale of 1-100 is 50 effectively "null", or equivalent to no data point at all?
I know the final answer lies in trial and error and in the meaning of our data, but as far as the algorithm goes, I'm trying to understand at what point I need to tip the scales between interest and disinterest for the algorithm to function properly.
The actual range does not matter, not for this implementation. 1-100 is OK, 0-1 is OK, etc. The relative values are all that really matters here.
These values are estimated by a simple (linearly) weighted average. Therefore the response ought to be "linear". It ought to match an intuition that if action X gets a score 2x higher than action Y, then X should be an indicator of twice as much interest in real life.
A decent place to start is to simply size them relative to their frequency. If click-to-conversion rate is 2%, you might make a click worth 2% of a conversion.
I would ignore the "Indifference" signal you propose. It is likely going to be too noisy to be of use.